document-clustering

Natural Language Processing (NLP) plays a crucial role in enhancing text classification and document clustering by enabling machines to understand, interpret, and generate human language. NLP algorithms help in identifying patterns, relationships, and sentiments within text data, which is essential for accurate classification and clustering. By utilizing techniques like tokenization, stemming, and named entity recognition, NLP can extract meaningful features from text, making it easier to group similar documents together. Additionally, NLP models like BERT and Word2Vec improve semantic understanding, leading to more precise categorization and clustering of textual data.